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In this work, chromium disilicide nanowires were synthesized by chemical vapor deposition (CVD) processes on Si (100) substrates with hydrous chromium chloride (CrCl 3 · 6H 2 O) as precursors. Processing parameters, including the temperature of Si (100) substrates and precursors, the gas flow rate, the heating time, and the different flow gas of reactions were varied and studied; additionally, the physical properties of the chromium disilicide nanowires were measured. It was found that single-crystal CrSi 2 nanowires with a unique morphology were grown at 700°C, while single-crystal Cr 5 Si 3 nanowires were grown at 750°C in reducing gas atmosphere. The crystal structure and growth direction were identified, and the growth mechanism was proposed as well. This study with magnetism, photoluminescence, and field emission measurements demonstrates that CrSi 2 nanowires are attractive choices for future applications in magnetic storage, photovoltaic, and field emitters.

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Summary The Levelized Cost of Electricity (LCOE) is a widely used indicator to assess the electricity cost of wind energy. The probabilistic approach and Monte Carlo technique are commonly used to capture the uncertainty of input variables in the LCOE calculation at the country level. However, a simple assumption for the range value of capacity factor (CF), a main input variable in the LCOE calculation, is inappropriate for analyzing an individual wind project. To apply the probabilistic approach in the assessment of electricity cost for individual wind projects, the study develops a new approach for estimating the CF distribution for a specific project. We focus on simulating the CF distribution for a specific wind farm based on the historical CF values computed from historical wind speed at the site. In comparison to traditional approaches, the proposed method provides a reliable range of capacity factor values and reduces the uncertainty in estimating energy generation by half for four example projects. As a result, the generated LCOE distribution is relatively closer to the real value of the projects, enhancing more realistic decisions for both project developers and policymakers.

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